Connection to TR-MAML [1]}:  Now, consider a setting where instead of stochastic gradients, we choose a full batch size and set \(g_i(x, y) = \ell _i(x, y) + \frac{\eta }{2} \Vert  y-x\Vert ^2\) . This gives us the one-step update \(y_i^{\star }(x) = x - \eta \nabla \ell _i (x, y)\) , which is exactly the formulation of TR-MAML. Therefore, our algorithm generalizes TR-MAML but adds flexibility, varying the step-size across tasks or using different update algorithms for individual tasks.
